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Aggregates Performance Management


When it Comes to Crushing, If You Don’t Measure It, You Can’t Manage It.

By Alan R. Bessen and Sam Sawant

This is the first of a three-part series on crushing measurement and analysis. – Ed.

While describing measurement techniques used to generate a multi-million dollar profit improvement, a new manager was asked, “How did they manage things before you got there?” He responded, “I guess they used the Force,” borrowing a Star Wars expression to indicate that previously there was no measurement system in place.

Addressing the issue of performance measurement, Dr. W. Edwards Deming, a well-known advocate of Statistical Process Control, is often quoted as saying: “In God we trust, all others bring data.”

It has also been said and repeatedly shown, even in the aggregates industry, that:

  • You can’t manage what you can’t control.
  • You can’t control what you don’t measure.
  • If you don’t measure it you can’t manage it.


“It,” in this discussion, is the entire production process as a system. As a quarry manager it is your job to acquire the data needed to manage the performance of your processing system. Relying on the “Force” does not work.

The following real-life example is one of dozens we could have chosen to illustrate the value and simplicity of obtaining the data necessary to effectively manage performance.

Basic measurements used to generate the multi-million dollar profit increase referred to above began with:

  • An AggFlow model comparing actuals to theoretical.
  • Direct field observation of the crushing process.
  • Crusher closed-side setting and condition measurement.
  • Belt cut gradation comparison and analysis.


The following issues were revealed and corrected:

  • Tertiary crusher output gradation results did not match the expected top size or the expected gradation for crusher configurations and measured settings.
  • Flow model validation revealed that tertiary screening was intentionally configured to allow production of a low-demand coarse product resulting in unanticipated volume of over-size feed to the fourth-stage crushers.
  • The combined effect of the above restricted the closed side setting and liner configuration options for both of the fourth-stage crushers.
  • The result was excessive recirculating load at the fourth stage significantly limiting the total plant infeed rate and the yield of key saleable products.


Correcting the above issues resulted in:

  • Fourth-stage production rate increase from less than 700 tph to more than 950 tph.
  • System production improvement due to infeed rate and yield enhancement resulted in added key product sales of over 250,000 tpy.
  • Reduced cost of production plus increased key product sales generated over $3 million in added profit during the first year.
  • No capital was required.


When You Measure It, You Are Managing It
Identifying where we are enables us to focus resources on where we want to go and on what it will take to get there. Comparing current capacity and yield results to original design targets provides a measure of performance effectiveness. However, for many reasons, it is likely that the original design targets are no longer valid.

Effective performance targets must be based on currently measured capability of the system to meet currently projected sales and cost expectations. Targets must be established for each commonly utilized process configuration (mode) and should represent the optimal expected production rate for each mode.

Without measurements needed to establish valid targets, we have no method of measuring degree of performance success or failure until well after the time that performance-limiting issues can be corrected. In fact, we probably don’t even know what potential results we should be expecting.

Inadequate or inaccurate measurement data will lead to invalid conclusions that do not realistically represent the current capabilities of your system. This results in invalid performance targets that are never achieved and are invariably ignored. Without comparison to a valid target it cannot be determined whether or not current performance is acceptable.

Accurately defining both the “as-is” and the “design” capability of your system is imperative. This is usually done by measuring and recording key process variables in short increments to define normal variance and maximum capacity within each process segment. Large variance may indicate a lack of capability within the processing system or an inability to adequately control the system. Or, it may simply indicate a mistake in the manner in which the system is being operated.
Measuring without valid comparison can, at minimum, be very confusing. As in the example, creating mathematical models of various process segments is essential to the performance measurement process.Within crushing and screening systems this requires creating a flow model representing equipment size, configuration and flow options. In the aggregates industry, AggFlow is the most commonly used software tool for creating a process flow model.

At minimum, AggFlow allows you to visualize flow options and the incremental effect of infeed rate, equipment setup and flow option changes on production. When validated by field measurement and sampling, it becomes one of the most critical elements of the performance management process. In fact, without AggFlow or equivalent flow-modeling capability, it is practically infeasible to simulate, much less effectively manage, the flow options available in most aggregate plants.

Click here for basic flow model tips.

Once the basic flow model has been developed results are used to direct field equipment observation, flow comparison and sampling needed to validate the model.

Click here for flow model validation examples.

Critical process elements are observed and measured resulting in identification of initial “bottlenecks” restricting performance within each process segment. This generally requires preliminary evaluation of equipment condition and assessment of “quick-fix” solutions to flow restrictions. Measurements are compared to flow model data often resulting both in adjustment to the model and to equipment configuration. Each process element potentially limits overall system capacity, so the effect of changing data on system capability must be updated with each measurement.

Advanced gradation sampling would include both crusher input and output. However, initial field sampling generally is directed toward crusher output gradation only. Initial crusher sampling is intended primarily as a comparison of output gradation results to results from the flow model. Crusher output gradation is the single most important field measurement in validating the flow model. It provides current “as-is” gradation results, which can be substituted within the AggFlow model, which is then recalculated based on the new field data.

This step is critical in establishing an accurate model as data included in the software is generally provided by the crusher manufacturer who presumes that crushers are:


The probability of all occurring in an aggregates plant is not even worth considering so do not be surprised when measured data does not match your calculated yield or capacity. The problem is seldom the AggFlow calculation; it is invariably what you put into the calculations that cause the variance.     

Field measurement results are compared to theoretical capacity and actual yield results throughout the flow model validation process. Benchmarks representing optimal capability are created for each production stage. Current results are then evaluated and compared to targets providing the information needed to effectively measure performance and to manage your process. Periodic revalidation of targets should occur until you are satisfied that specific bottlenecks limiting performance in each operating mode have been identified, corrected or accepted as not practical to change.

The design maximum capability for each operating mode is established during the flow model validation and field measurement process. The difference between measured “as-is” results and the theoretical design capabilities of each segment will indicate potential for improvement.

Cost associated with improvement options requiring process, equipment, procedural or scheduling changes can be projected and compared against expected benefit. Major equipment improvements should generally be implemented one at a time with results verified to document both new “as-is” performance and new targets.

Knowing What You’ve Got
There is a point in every operation that yields the highest percentage of quality in-demand product at the lowest cost with the least amount of waste. Accurate evaluation of the present state of a process is necessary to achieve optimal performance. The more accurate and timely that data is, the higher the likelihood intended results will be achieved.

Performance Management, allowed to diminish to a “seat of the pants” approach, results in millions of dollars in lost profit and misallocated upgrade capital spent replacing systems that operate at 40 percent to 60 percent of capability; often only because they are improperly utilized or maintained.

The described measurement technology and procedures have been applied successfully for many years. They work and are very easy to understand and put into operation.

Technology makes it possible to further simplify and automate critical aspects of both process measurement and process control. However, successful implementation of measurement -based Performance Management requires a cultural commitment to both measuring and to managing performance.

It is important to remember that even though the field measurement techniques are extremely easy and effective, it is the management step that will improve performance, reduce cost and increase profit.

Identify What You Need
Managing performance, maximizing saleable tons and minimizing surplus inventory reduces cost and always increases profit.

In the next segment of this series we will expand on measurement concepts beyond plant and equipment to include sales, inventory and cost. We will discuss:

  • Identifying and minimizing system level limitations.
  • Pursuing market demand for key products.
  • Minimizing waste and by-product.
  • Managing inventory surplus and shortage.


The final segment will describe both manual and technology based methods of measurement and management of aggregates performance and will include:

  • Advanced performance measurement using real-time belt scale systems.
  • Basic trending and control charts applications showing acceptable and problematic variability.
  • Statistical methods of establishing and monitoring KPI’s.
  • Web based management interface, reporting and alarming technology.


Alan R. Bessen, P.E., is a process-design, automation and performance optimization consultant with more than 35 years of experience in the aggregates and mining industries. He can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it..

Sam Sawant has more than 30 years of experience developing new and innovative products for the aggregate and mining industries. He holds two masters degrees in Mechanical Engineering, and worked with Nordberg Inc. before starting Innotech Solutions in January of 2000. He has extensive experience analyzing mining and aggregate circuits, proposing substantial improvements and implementing the changes that have met or exceeded customers’ expectations. He can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it.